[1]} perform a large-scale query log analysis using the data collected by the Bing search engine to identify a taxonomy of different clarification types required in open-domain IR. The taxonomy leads to a small number of question templates.
Then, a rule-based model is proposed for generating clarifying questions. The model selects and fills out question templates using query aspects and query entity type information.
Furthermore, two neural question generation models are proposed. They are trained using supervised and reinforcement learning on the weak supervision data generated by the rule-based model.
